The volume of data that enterprises can accumulate today is staggering. Traditional systems are not intrinsically designed to handle the scale of data required for present-day data analytics. Therefore, traditional data processing systems are unable to handle/process the data in a timely or efficient manner. The traditional systems use significant memory space for storing and significant computing powers for processing the data. The precious memory space of the underlying computer system, since now being used for storing and processing the large volume data, cannot be used for other operations. This places an increased burden on the computer system that processes the data, thereby decreasing the performance of the computer system. Furthermore, traditional systems may be unable to process the ingested raw data properly before transmitting them over the computer networks. Such raw data or coarse data are larger in size compared to specifically structured or processed data. These raw data or coarse data that are transmitted over the network creates a significant strain on the network, leading to network bottlenecks.
Furthermore, traditional systems tend to send data to users universally. For example, the traditional systems may send a report to every single user over the network. However, sending the report to every single user of the systems may create a significant burden on the network because the systems would send the report simultaneously to a significant number of users over the network. Furthermore, if the report is not read or accessed by some of the users, the network resources that are used for transmitting the report to these users are wasted. This further intensifies the network bottleneck.